Rooting the animal tree of life

Yuanning Li1,3, Xingxing Shen3, Benjamin Evans2, Antonis Rokas3, and Casey W. Dunn1*

1Department of Ecology and Evolutionary Biology, Yale University

2Yale Center for Research Computing, Yale University

3Department of Biological Science, Vanderbilt University

* Corresponding author,

Abstract

Phylogenomic studies based on hundreds to thousand of genes have greatly improved our understanding of evolutionary relationship among major animal lineages. However, a considerable debate still remains regarding the first diverging animal lineage, with Ctenophora-sister and Porifera-sister emerging as the two primary hypotheses, comprising one of the most difficult node to resolve in animal tree of life. Here, we systematically explore the mechanisms (evolutionary models, gene sampling and outgroup choice) that potentially cause the incongruent results by synthesizing data and results from all previous phylogenomic analyses, and perform new analyses in these data in a consistent manner to characterize the differences between studies. We find the gene sampling varies across studies, indicating that different regions of the genomes are sampled with little overlap among data matrices. More Importantly, We find Porifera-sister can only be recovered with more constraints, including using site-heterogeneous CAT model with inclusive of the most closely related outgroups in several matrices, whereas Ctenophora-sister is recovered in all other cases. We next quantify the support of phylogenetic signal over the two hypotheses and find substantial differences of distribution of phylogenetic signal between models and outgroup choice (especially CAT models). Last, we show that recoding can be a problematic method for addressing compositional variation in phylogenetic analyses. Our results provide a comprehensive overview of current understanding of the first diverging animal branch and why different studies yield different answers. Rather than embracing one hypothesis over another, we hope our efforts provides an integrative overview of the challenge and provides direction for future studies to address difficult phylogenetic problems.

Introduction

Phylogenomic analyses with genome-scale data have the potential to revolutionize our understanding of the tree of life. However, over the past decade, there has been considerable debate about the position of the root of the animal phylogeny, with Ctenophora-sister (comb jelly) and Porifera-sister (sponges) (Fig XXOverview) emerging as the two primary hypotheses. Historically, there was little debate about the root of the animal tree of life and Porifera-sister was widely accepted though rarely tested. In contrast to the lack of debate about the position of Porifera, there has long been uncertainty about the relationship of Ctenophora to other animals1. An understanding of the first diverging animal lineage is critical for scientists to reconstruct events that occurred early in animal evolution, especially for the evolution of complex structures such as cell types, nervous systems, and developmental patterns. However, a wide consensus of the position of animal has not been reached despite multiple efforts.

The first phylogenomic study to include ctenophores2 suggested a new hypothesis, now referred to as Ctenophora-sister, that ctenophores are our most distant animal relative. Since then many more studies have been published and extensive reanalyzed, some supporting Ctenophora-sister, some Porifera-sister, and some neither. As it has become clear that this is a very difficult phylogenetic challenge, and the problem has become better characterized, it has become an interesting test-case to phylogenetic biologists beyond those concerned with this particular biological problem. Work has been hindered, though, because it has been difficult to directly compare results across studies and synthesize findings to understand the broader patterns of support. Here we synthesize data and results from all previous phylogenomic analyses that tested Ctenophora-sister and Porifera-sister, and reanalyze these data using standardized methods, and perform new analyses to characterize differences between studies. We hope that this effort provides an integrative overview of the challenge and provides direction for future studies. We also hope that the work we have conducted here, including consolidating all the datasets in one place with consistent formats and species names, will enhance the technical value of this interesting question to methods-focused investigators that look to develop methods to address difficult phylogenetic problems.

Fig XXOverview. (A) The Ctenophora-sister hypothesis posits that there is a clade (designated by the orange node) that includes all animals except Ctenophora, and that Ctenophora is sister to this clade. (B) The Porifera-sister hypothesis posits that there is a clade (designated by the green node) that includes all animals except Porifera, and that Porifera is sister to this clade. Testing these hypotheses requires evaluating the support for each of these alternative nodes. (C) The animals and their outgroup choice, showing the three progressively more inclusive clades Choanimalia, Holozoa, and Opisthokonta (designated by the blue node). (D) A ctenophore. (E) A sponge.

Variation across studies

Models of molecular evolution

Models of molecular evolution have several components that each consider different aspects of the evolutionary process. The models that have been used to model protein evolution in studies of the deep phylogeny have largely differed according to three components: the exchangeability matrix \(E\), the rate of evolution, and the state equilibrium frequencies \(\Pi\). A detailed description of each component is in Supplementary text. Briefly, (1) the exchangeability matrix \(E\) describes the rate at which one amino acid changes to another. Exchangeability matrices have been used in the studies under consideration here include: Poisson (or F81), WAG, LG, GTR, as they have been used regularly in phylogenomic analyse; (2) While the exchangeability matrix describes the relative rate of different changes between amino acids, the actual rate can be further scaled, such as site homogeneous rates and Gamma-rate heterogeneity; (3) the vector of equilibrium frequencies \(\Pi\) describes the stationary frequency of amino acids, such as empirical site-homogeneous, estimated site-homogeneous, CAT site-heterogeneous3, and C10-C60 models (C models). (4) data-recoding method recode 20 amino acids are recoded into six groups based on function to account for both compositional heterogeneity and substitution saturation. Several recoding strategies have been proposed, including Dayhoff 6-state recoding, S&R 6-state recoding, KGB 6-state recoding based on exchangeability matrix of models4.

Models can be assembled by selecting different options for all these different components. The models that are applied in practice area heavily influenced by engineering and computational costs, as well as convention. For example, on the questions considered here, Poisson and GTR exchangeability matrices have only been used in combination with CAT site-heterogeneous models of equilibrium frequency. LG and WAG exchangeability matrices have only been used with site homogeneous estimates of equilibrium frequency. This is further confused by the abbreviations that are used for models. Papers often discuss CAT and WAG models as if they are exclusive, but these particular terms apply to non-exclusive model components– CAT refers to variation across sites and WAG a particular exchangeability matrix. CAT is generally shorthand for F81+CAT and WAG is shorthand for WAG+homogeneous equilibrium frequency estimation. One could, though, run a WAG+CAT model. To avoid confusion on this point, we always specify the exchangeability matrix first, followed by modifiers that describe the accommodation of heterogeneity in equilibrium frequencies (e.g., CAT) or rate (e.g., Gamma). If there are no modifiers, then it is implied that site homogeneous models are used.

Gene sampling

High-throughput sequencing is leading to exciting new data matrices for phylogenomic analyses, potentially including hundreds or thousands of genes to explore the root of metazoan phylogeny (Table 1). Although the number of sampled genes might be different across studies, it is reasonable to expect that a conserved core set of orthology genes (OGs) should be present across most animal genomes (e.g., animal BUSCO genes5). Surprisingly, gene composition and matrix overlap has been rarely assessed across studies and most studies relied on their own bioinformatic pipeline for determination of OGs (Supplementary text) for animal phylogeny.

Outgroup and ingroup taxon sampling

Outgroup selection (the taxa used to root the ingroup taxa) can potentially affect the phylogenetic inference of the root position of animal phylogeny (Fig XXOverview.C). Several studies have progressively removed more distantly related outgroup taxa to test the effect of outgroup selection to ingroup topology???,6. Three progressively more inclusive clades have often been investigated, including Choanomalia (Metazoa plus most closely related Choanoflagellata), Holoza (Choanimalia plus more distantly related holozoans) and Opisthokonta (Holozoa + Fungi). It has been suggested the inclusion of more distant related (e.g. Fungi) outgroup taxa tend to recover Ctenophora-sister, whereas high support of Porifera-sister often recovered when more closely related outgroup taxa were used with site-heterogeneous CAT models6. In contrast of outgroup selection, sensitivity to ingroup sampling has received less attention than sensitivity to outgroup sampling. This may be because results have tended to be more sensitive to outgroup sampling.

Data-recoding

Compositional heterogeneity (lineage-specific differences in amino acid frequencies) was long thought has a major effect in resolving the animal tree of life, especially for the deep animal phylogeny7. Feuda et al. (2017)4 has proposed to use data-recoding method to reduce compositional heterogeneity in data matrices. By discarding information on which specific amino acid is found at each site in each species and instead focusing on which groups those amino acids belong to, they sought to reduce the impact of differences between species in which particular amino acids within those groups are most frequent. By reanalyzing Chang et al. 20158 and Whelan et al. 20159, they report that posterior predictive analyses indicate recoded analyses have better model adequacy than non-recoding analyses, and that Porifera-sister was strongly favored under all recoding strategies using GTR-CAT models4. However, how the recoding methods impact phylogenetic inference has not been well explored. Moreover, a recent simulation study has suggested that non-recoding approaches significantly outperformed recoding strategies10.

Overview of published analyses

Here, we synthesize data and results from all previous phylogenomic analyses that tested Ctenophora-sister and Porifera-sister, including 17 data matrices from 12 studies (Table 1).

Table 1. Overview of data matrices used in this study
manuscript matrix gene_partitions data_size number of taxa
Dunn2008 Dunn2008 150 21152 64
Philippe2009 Philippe2009 129 30257 55
Hejnol2009 Hejnol2009 1487 55594 94
Nosenko2013 Nosenko2013_nonribo_9187 35 9187 63
Nosenko2013 Nosenko2013_ribo_11057 78 11057 50
Nosenko2013 Nosenko2013_ribo_14615 87 14615 71
Ryan2013 Ryan2013_est 406 88384 61
Moroz2014 Moroz2014_3d 114 22772 46
Whelan2015 Whelan2015_D1 251 59733 76
Whelan2015 Whelan2015_D10 210 59733 70
Whelan2015 Whelan2015_D20 178 81008 65
Borowiec2015 Borowiec2015_Best108 108 41808 36
Borowiec2015 Borowiec2015_Total1080 1080 384981 36
Chang2015 Chang2015 170 51940 77
Simion2017 Simion2017 1719 110602 97
Whelan2017b Whelan2017b_full 212 68062 80
Whelan2017b Whelan2017b_strict 59 49388 76

Matrix taxon composition

Taxa sampling plays a critical role in phylogenetic inference, especially for the taxa whose phylogenetic position that needs to be explored. However, several data matrices have relatively skewed taxon sampling, with more than 50% taxa sampled in bilaterians compared to other major animal lineages (Fig XXTaxon_composition). These skewed data matrices include two studies constructed from ESTs (Dunn20082 and Hejnol200911) and one study from whole-genome data (Borrowiec201512 ). Importantly, several earlier studies only include less than five ctenophore species. The rest data matrices have relatively balanced taxon composition, although the total number of sampled taxon varies a lot. In general, increasing taxon sampling is broadly accepted to improve phylogenetic inference and aid in the placement of animal root position. Thus, it should be noted that Whelan201713 (has the most Ctenophore taxa) and Simion201714 (contains the most taxon sampling) have much broader taxon sampling than other data matrices (Table1, Fi. XXTaxon_composition).

Fig XXTaxon_composition. Each of the primary matrices considered here, color coded by taxon sampling. Horizontal size is proportional to the number of genes sampled, vertical size to the number of taxa sampled.

Matrix gene composition and overlap

Gene sampling plays an essential role in phylogenomic analyses, and most often rely on the concatenation of sampled genes for deep animal phylogeny. It has been suggested that the concatenation assumes that phylogenetic signal from genes that do not share the species phylogeny will be overwhelmed by the signal from the majority of genes whose genealogy mirrors that of the species evolutionary history15. However, large phylogenomic data matrices potentially contain genes with conflicting or weak phylogenetic signal that can lead to incongruence phylogenomic results, especially when analyzing deeper node represents ancient divergence in the tree16. Thus, it is important to evaluate the gene composition and matrix overlap to explore level of congruence of gene sampling across different matrices. It should be noted that early studies relied on EST via Sanger sequencing contains large missing data compared to other ones (e.g., Dunn2008 and Hejnol2009).

We first used the Metazoa BUSCO dataset to evaluate gene composition from all data matrices and the result is unexpected. The animal BUSCO dataset contains 978 single-copy OGs (identified from 64 animal taxa) that are most likely shared by most animal genomes, and are commonly used to assess the completeness of genome or transcriptomic assemblies5. Moreover, the BUSCO dataset also are frequently used as markers in many large-scale phylogenomic studies (e.g., yeasts17, spiders18 ). Thus, it is reasonable to expect that these conserved genes should present at a higher percentage than non-BUSCO genes in most matrices. However, to our surprise, we found that gene partitions from all matrices comprised less than 50% of BUSCO genes, even with several data matrices contain less than 20 percent (Fig. XXBUSCO_annotations).

Previous reanalyses have mainly focused on outgroup selection and/or model fits in different data matrices, and the matrix overlap across different studies has been rarely investigated. Our initial intent of comparing matrix overlap across matrices was to construct a new matrix with shared taxon and gene sampling to test specific hypotheses about differences in support. The hypothesis is that although the bioinformatics pipeline used for OG determination is different, most larger data matrices should share a core set of OGs. However, our results show that surprisingly little overlap across all the data matrices from different studies (Fig XXAlignment overlap). Thus, these results suggested that gene sampling largely varies across studies, indicating that different regions of the genomes are sampled with little overlap among different data matrices.

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Fig XXBUSCO_annotations. BUSCO annotation and representation in each data matrix. (A). The number of partitions with BUSCO annotations in each matrix, relative to the number of partitions. (B). The percentage BUSCO annotations in each matrix.

Fig XXGene_composition. Each of the primary matrices considered here, color coded by the types of genes sampled (XX Ribosomal proteins, etc). Horizontal size is proportional to the number of genes sampled, vertical size to the number of taxa sampled.

Fig XXAlignment overlap. Pairwise overlap between each of the primary matrices considered here. Horizontal size is proportional to the number of genes sampled, vertical size to the number of taxa sampled. The horizontal intersection shows the proportions of shared genes, the vertical intersection shows the proportions of shared taxa.

Support for Porifera-sister and Ctenophora-sister

Here we summarized all the 131 previous phylogenetic analyses from 15 studies (Fig. support_published_analyses and Table perviouly_published_analyses). Among these 15 studies, the main conclusions of five studies strongly in favor of Porifera-sister and 10 supports Ctenophora-sister (TablexxPAPERS). Moreover, three of them are studies of reanalysis without generating any new data matrices. We found that all past phylogenetic analyses strongly support of Ctenophora-sister once site-homogeneous models were used, no matter what outgroup choice, phylogenetic inference and whether data-recoding are used. The support for Porifera-sister increases with the exclusion of more distantly related outgroups with the use of site-heterogeneious CAT (Poisson+CAT+G or GTR+CAT+G) models. It should be noted that many BI analyses with CAT model are not well resolved (either not reach convergence or do not provide significant support for the key node of the root of animal phylogeny) mainly due to computational limits of CAT models employed in PhyloBayes. The strong support of Porifera-sister can only be recovered from analyses using Poisson+CAT+G and recoding methods with GTR+CAT+G models in several matrices. Although GTR+CAT model generally has better model adequacy than Poisson+CAT or site-homogeneous models from previous studies4,6, Porifera-sister is not always supported since several analyses strongly in favor Ctenophora-sister even with only Choanofalgellates are used as outgroups (Fig. support_published_analyses).

Table XX. Overview of previous phylogenomic studies related to the root position of animal phylogeny
manuscript doi citation new_data new_analyses Main conclusion
Dunn2008 10.1038/nature06614 NA TRUE TRUE Ctenophora-sister
Philippe2009 10.1016/j.cub.2009.02.052 NA TRUE TRUE Porifera-sister
Hejnol2009 10.1098/rspb.2009.0896 NA TRUE TRUE Ctenophora-sister
Nosenko2013 10.1016/j.ympev.2013.01.010 NA TRUE TRUE Porifera-sister
Ryan2013 10.1126/science.1242592 NA TRUE TRUE Ctenophora-sister
Moroz2014 10.1038/nature13400 NA TRUE TRUE Ctenophora-sister
Whelan2015 10.1073/pnas.1503453112 NA TRUE TRUE Ctenophora-sister
Borowiec2015 10.1186/s12864-015-2146-4 NA TRUE TRUE Ctenophora-sister
Chang2015 10.1073/pnas.1511468112 NA TRUE TRUE Ctenophora-sister
Pisani2015 10.1073/pnas.1518127112 NA FALSE TRUE Porifera-sister
Whelan2017 10.1093/sysbio/syw084 NA FALSE TRUE Ctenophora-sister
Simion2017 10.1016/j.cub.2017.02.031 NA TRUE TRUE Porifera-sister
Shen2017 10.1038/s41559-017-0126 NA FALSE TRUE Ctenophora-sister
King2017 10.1016/j.cub.2017.08.054 NA FALSE FALSE NA
Whelan2017 10.1038/s41559-017-0331-3 NA TRUE TRUE Ctenophora-sister
Feuda2017 10.1016/j.cub.2017.11.008 NA FALSE TRUE Porifera-sister

Fig support_published_analyses. A total of 131 analyses were transcribed from the literature (Table perviouly_published_analyses.tsv).

New analyses of published matrices

One of the challenges of interpreting support for the placement of the animal root across studies is that different programs, software versions, and settings have been used across studies, and phylogenetic analysis decisions have been approached in very different ways. Here we reanalyze the primary matrices from each study under consistent conditions with IQtree under a panel of evolutionary models. We selected this tool because it has greater model flexibility, computational time and accuracy than other tools in maximum likelihood (ML) framework. Importantly, we also include a CAT-like, site-heterogeneous C10 - C60 (C) models that are implemented in both ML framework in our analyses and it is interesting to compare results between C model to CAT models since it has never been used in any matrices analyzed here.

Here we reanalyzed 17 from 15 studies that are used as main conclusions from their original paper (Table datasets). We also progressively trimmed each data matrix with different level of outgroup choice if applicable. We first tested a variety of models for each matrix and compared the relative fit of site-homogeneous and site-heterogeneous C models using ModelFinder in IQtree (except only LG+C60+F+G models are used large matrices including Hejnol2009, Borrowiec_Total1080 and Simion2017 matrices). We found in all cases, WAG/LG+C60 models fit these matrices better than the site-homogeneous models under BIC criteria and we then inferred support under the best-fit model from IQtree. It should be noted that we didn’t include GTR + C models in model testing since it is computational too expensive. The GTR+C models requires to estimate up to over 10,000 (e.g. GTR+C60 contains 234*60 parameters) free parameters inferred from matrices, whereas only several hundred parameters needs to estimate in site-homogeneous + C models. We then analyzed every matrix under a panel of standard site-heterogeneous and site-homogeneous models, including WAG+G, GTR+G and Poisson+C60+F+G. Moreover, we also used the best-fit model identified above with the removal of all distantly related outgroups and only include Choanoflagellata as outgroups.

With the exception of Moroz2014 matrix that supports neither Porifera or Ctenophora-sister in every models, all analysis conducted herein in IQtree strongly supported the ctenophore-sister hypothesis, even with CAT-like, site-heterogeneous C models with the most closely related outgroups.

matrix clade result model_summary
Borowiec2015_Best108 Choanimalia Ctenophora-sister WAG+C60
Chang2015 Holozoa Ctenophora-sister LG+C60
Dunn2008 Opisthokonta Ctenophora-sister WAG+C60
Moroz2014_3d Choanimalia Unresolved WAG+C60
Nosenko2013_nonribo_9187 Opisthokonta Unresolved WAG+C60
Nosenko2013_ribo_11057 Choanimalia Unresolved WAG+C60
Nosenko2013_ribo_14615 Opisthokonta Unresolved WAG+C60
Ryan2013_est Opisthokonta Ctenophora-sister WAG+C60
Whelan2015_D1 Opisthokonta Ctenophora-sister LG+C60
Whelan2015_D10 Opisthokonta Ctenophora-sister WAG+C60
Whelan2015_D20 Opisthokonta Ctenophora-sister WAG+C60
Whelan2017_full Holozoa Ctenophora-sister LG+C60
Whelan2017_strict Choanimalia Ctenophora-sister LG+C60

Table XXModelfinder. The models selected by modelfinder for each matrix.

Comparison of iqtree and phylobayes results

Site heterogeneity in equilibrium frequency has been a major concern in tests of Ctenophora-sister and Porifera-sister. This has been addressed with CAT models. IQtree provides a new family of C models that also address site heterogeneity. Given the extensive computational cost and concerns about overparameterization of CAT models19, we compared iqtree C results to CAT results for a subset of matrices to see if they give consistent results. This would be of technical interest because it would reduce the cost of accommodating compositional heterogeneity in future analyses. For computational efficiency, we first only applied Poisson+CAT models in every matrix with Choanoflagellata as outgroup. It should be noted that most Phylobayes runs were converged, although several large matrices have not reached convergence after at least a month’s computational time.

Interestingly, we found strong supports for both hypotheses when using Poisson-CAT model in Choanimalia matrices. Similar to ML analyses, we found no strong support for either hypothesis in Moroz2013_3d matrix. Consistent with previous study, we recovered strong support of Porifera-sister in matrices of Philippe2008, Ryan2013, Nosenko2013 and Whelan2015. Interestingly, we also found a strong support of Porifera-sister in two Whelan2017 matrices and a weaker support of subsampled matrix of Simion2017. Ctenophora-sister were strongly supported in all other matrices. For the matrices have conflicted results between Poisson-CAT and C models, we further ran Poisson+CAT models with inclusion of more distantly related outgroups to evaluate how outgroup choice may affect phylogenetic inference under CAT model. We found strong support of Porifera-sister in holozoa matrices and Ctenophora-sister in all analyzed Opisthokonta matrices, indicating that the support of Porifera-sister is increasing with inclusive of the more closely related outgroups. Thus, our results show that both model choice and outgroup sampling play a major role in incongruent results of the root position of animal phylogeny in several key data matrices.

Phylogenetic signal

The distribution of phylogenetic signal over the root position of animal phylogeny has never been quantified with site-heterogeneous models. To further explore the effect of model selection and outgroup choices in the root position of animal phylogeny, we next quantify the support of phylogenetic signal over two alternative hypotheses (T1: Ctenophora-sister (Fig. 1A); T2: Porifera-sister (Fig. 1B)) to three key data matrices with different outgroup choice and models in both ML and BI framework (Fig XXPhylogenetic signal). By calculating gene-wise log-likelihood scores between T1 and T2 for every gene (\(GLS) or site (\Delta\)SLS) in each matrix, we found that Ctenophora-sister had the higher proportions of supporting genes in every analysis when using site-homogeneous models (LG or WAG in both IQtree or PhyloBayes). Moreover, the outgroup choice has little impact on the distribution of the support of phylogenetic signal in site-homogeneous models. This finding is largely consistent with the past study that majority of phylogenetic signal is strongly favored in Ctenophora-sister hypothesis with site-homogeneous models in other data matrices20.

In contrast, the distribution of phylogenetic signal has never been quantified in site-heterogeneous models and compared with site-homogeneous models. Although Ctenophora-sister also had the higher proportions of supporting genes with C models, we found that the phylogenetic signal decreases in many genes using C models compared to site-homogeneous models, especially in two matrices from Ryan_2013 that nearly 30% of genes changed from strong to week signal ($lnL<2) (Fig. phylogenetic_signal B). It is not clear why the phylogenetic signal decreases in many genes with a better fitting model as suggested by model testing in IQtree, but our results indicated the C models lack of phylogenetic signal compared to site-homogeneous models in the node of root position of animal phylogeny. In contrast to the C models, the phylogenetic signal largely increased in Poisson-CAT model and more importantly, the outgroup choice has a major effect of the distribution of phylogenetic signal. We found that support for Ctenophora-sister largely decreases and Porifera-sister increases (~ 20% of sampled gene partitions) when exclusive of more distantly related outgroups with the Poisson-CAT models. The results here are further corroborated by our phylogenomic analyses in BI, which the support of Porifera-sister increases in Holozoa and Choanimalia matrices.

We also examined the level of congruence of genes that are supported in each hypothesis across different analyses in Whelan2017 matrices. Consistent with the summary of $GLS analysis, We found gene population that supports in either hypothesis are largely congruent between site-homogenous and C models, whereas little overlap was observed in Poisson-CAT models (especially with different outgroup choice). In other words. our results strongly indicated that the distribution of phylogenetic signal of the root of animal phylogeny is largely sensitive to model selection (site-homogeneous vs. site-heterogeneous models). More importantly, the site-heterogeneous CAT model is also more sensitive to the outgroup choice than site-homogeneous and C models in these key data matrices. Thus, it is clear that recovering Porifera-sister requires more constrains, including more stringent model selection, outgroup choice, and even with gene sampling.

Fig XXPhylogenetic signal. The distribution of phylogenetic signal for two alternative topological hypotheses on the earliest-branching animal lineage. Proportion of genes supporting each of two alternative hypotheses for each of ### Bias of data-recoding There is growing interest in data-recoding and previous study4 hoped that data-recoding would reduce potential artifacts due to differences across species in amino acid frequencies. They report that posterior predictive (PP) analyses21 indicate 6-state recoded analyses have better model adequacy than 20-state amino acid analyses, and “Porifera-sister was favored under all recoding strategies” in Whelan2015_D20 and Chang2015 data matrices. Here we focus on two aspects of Feuda et al. First, we found that many of their recoded analyses are actually unresolved (i.e., without strong support for either Porifera-sister or Ctenophora-sister), and that the analyses with the best posterior predictive scores do not provide strong support for Porifera-sister (Fig XXRecoidng_summary). Second, we created four new random recoding schemes by shuffling the amino acids in the SR-6 scheme (see methods). When we applied each of these randomized codes to the Whelan matrix and analyzed them under the CAT-GTR+G model with phylobayes-mpi, we observed similar results as for the empirical SR-6 recoding. Like SR-6 recoding, random recoding increases support for Porifera-sister and improves the apparent adequacy of models to explain heterogeneity of states across taxa (PP taxon hetero mean and max, Figure XXRandom_recodingB). Thus, our analyses show the impact of recoding is largely due to discarding information, not accommodating variation in amino acid composition. These findings indicate that it is premature to accept Porifera-sister and reject Ctenophora-sister using data-recoding methods and recoding can be a problematic method for addressing compositional variation (see more detailed discussion in supplementary text).

The current state of understanding

Next steps

Conclusion

Methods

All files and bioinformatic commmands associated with this analysis are available at https://github.com/caseywdunn/animal_root

Data selection and wrangling

We retreived matrices from each publication (Table XX), storing the raw data in this manuscript’s version control repository. We manually edited some minor formatting to make the batch processing of the matrices en masse, e.g. standardizing the formatting of charset blocks. All changes made are tracked with git.

Matrix comparison and annotation

Taxon name reconciliation

We programatically queried the NCBI Taxonomy database to standardize names of samples in each matrix. We also use a table where manual entries were needed (Supp Table XX, reconciliation/taxonomy_info/manual_taxonomy_map.tsv), e.g. authors of original matrix indicate species name in original manuscript. For a table summarizing all samples and their new or lengthened names, see Table XX(reconciliation/taxonomy_info/taxon_table.tsv).

Sequence Comparisons

Using the partition files for each matrix, we isolated each sequence for each taxon from each partition. Because many of the matrices had been processed by the original authors to remove columns that are poorly sampled or highly variable, these matrix-derived sequences can have deletions relative to the actual gene sequences.

We used DIAMOND22 to compare each sequence to all others using default diamond blastp parameters. We further filtered DIAMOND results such that we retained hits for 90% of partitions (pident > 78.0, eValue < 1e-15, no self->self). We ran BUSCO with default parameters for all sequences against the provided metazoa gene set. We also ran a BLAST+ blastp search against the SwissProt [cite] database, filtering such that we retain at least one hit for ~97% of partitions (pident > 50.0, eValue < 1e-15).

Partition Network

We used the sequence similarity comparisons described above to compare partitions.

We constructed a network with Python and NetworkX23 v2.2 where each node is a partition and each edge represents a DIAMOND sequence-to-sequence match between sequences in the partitions. We extracted each connected component from this network. We further split these components if the the most connected node (i.e. most edges) had two times more the standard deviation from the mean number of edges in the component it is a member of and if removing that node splits the component into two or more components. We then decorated every node in the partition network with the most often found SwissProt BLAST+ result and BUSCO results to see which components contain which classes and families of genes. See Table XX [partition_network_summary table] for a summary tally of each part of the comparison.

Phylogenetic analyses

Phylogenetic analyses in IQtree

To investigate the phylogenetic hypotheses and distribution of phylogenetic signal in studies aiming to elucidate the root of animal position, we considered 17 data matrices from all phylogenomic studies that were constructed from EST, transcriptomic or genomic data (Table). Because different choices of substitution models could largely influence phylogenetic inference of the placement of the root of animal tree of life (e.g. site-heterogeneous vs. site-homogeneous models), we first investigated model-fit from each matrix using ModelFinder24 in IQtree25, including site-heterogenous C10 to C60 profile mixture models as variants of the CAT models in ML framework (C10-C60 model were included for model comparison via -madd option). We included models that are commonly used in previous analyses, including site-homogeneous poisson, WAG, LG, GTR models plus C models in the model testing. For computational efficiency, the GTR with C models were not included in model testing since it requires to estimate over 10,000 parameters. For large matrices like Hejnol2009, Borrowiec2015 and Simion2017, model testing is also not computational feasible so only LG+C60 models were used since LG/WAG+C60 models were suggested as the best-fit model in all other matrices.

We then reanalyzed each matrix under a panel of evolutionary models, including WAG+G, GTR+G, poisson+C60 and associated best-fit model identified above. Nodal support was assessed with 1000 ultrafast bootstrap replicates for each analysis. Because of the large size of Hejnol2009 and Simion2017, it was not computationally feasible to analyze the whole matrix using the C model or CAT site-heterogeneous models. To circumvent his limitation, we reduced the data size from their full matrices to facilitate computational efficiency for site-heterogeneous models. For Hejnol2009 matrix, we instead used the 330-gene matrix constructed by Hejnol et al. 200911, since the main conclusion for their study is based on this subsampled matrix; For Simion2017 matrix, we only included most complete 25% of genes (genes that were present in less than 79 taxa were removed; 428 genes were kept). It should be noted that the main conclusion of Simion et al. 2008 was also based on selection of 25% of genes for their jackknife approach.

Outgroup taxa sampling with C10-C60 and CAT models

Because different choices of outgroups could also affect phylogenetic inference as suggested in previous analyses, we parsed the full data matrices into three different types of outgroups: Choanimalia , Holozoa and Opisthokonta. These datasets include the same set of genes but differ in the composition of outgroup species. Choanimalia only includes choanofagellates as outgroup; Holozoa also includes more distantly related holozoans; Opistokonta also includes Fungi. For each Choanimalia data matrice, both C models IQtree and Poisson-CAT models in PhyloBayes were conducted. The maximum likelihood analysis was performed using the best-fit substitution model identified as above and nodal support was assessed with 1000 ultrafast bootstrap replicates using IQtree. Moreover, bayesian inference with the site-heterogeneous poisson-CAT model was done with PhyloBayes MPI26. To minimize computational burden, CAT-GTR models were only performed in the.

For results of choanozoa matrices indicated a strong support that sponges are the sister group to the remaining Metazoa using CAT model, bayesian inference with Poisson-CAT model was also conducted to Holozoa and Opisthokonta data matrices with the same settings as above. For all the analyses with Poisson+CAT models in PhyloBayes, two independent chains were sampled every generation. Tracer plots of MCMC runs were visually inspected in Tracer v1.6 to assess stationarity and appropriate burn-in. Chains were considered to have reached convergence when the maxdiff statistic among chains was below 0.3 (as measured by bpcomp) and effective sample size > 50 for each parameter (as measured by tracecomp). A 50% majority‐rule consensus tree was computed with bpcomp, and nodal support was estimated by posterior probability. For those matrices that were not converged, PhyloByes analyses were run for at least one month.

phylogenetic distribution of support

To investigate the distribution of phylogenetic signal in data matrices, we considered three major data matrices from 3 studies that had different topology between ML and BI using CAT model in our reanalysis, including Philippe2008, Ryan2013, and Whelan_2017 data matrices. We examined two hypotheses: Ctenophora-sister and Porifera-sister to rest of metazoans, under both ML and BI frameworks with different outgroup schemes (Choanomalia and Opisthokonta). For ML analysis in each dataset, site-wise likelihood scores were inferred for both hypotheses using IQtree (option -g) with the same best-fit model identified above, and site-homogeneous WAG models. The two different phylogenetic hypotheses passed to IQtree (via -z) were the corresponding tree that the ctenophore as the sister lineage tree and the corresponding tree that was modified to have sponges as the sister to all other metazoans. The constraint trees were conducted in a customized R script. The numbers of genes and sites supporting each hypothesis were calculated with IQtree output and Perl scripts from Shen et al. 201720. For BI analysis, we only considered the Philippe_2009 and Whelan_2017 datasets.

Performance of data-recoding

All code used for the analyses presented here is available in a git repository at https://github.com/caseywdunn/feuda_2017_response. The randomized recoding analyses are in the ../trees_new/recoding/feuda_2017_response/alternative subdirectory of the git repository.

The original SR-6 recoding scheme is “APST CW DEGN FHY ILMV KQR27, where spaces separate amino acids that are placed into the same group. This recoding is one member of a family of recodings, each with a different number of groups, based on clustering of the JTT matrix. The other recoding used by Feuda et al., KGB-6 and D-6, are based on different matrices and methods28.

The alt_recode.py script was used to generate the randomized recoding schemes and apply the recodings to the data. To create the randomized recoding schemes, the amino acids in the SR-6 recoding were randomly reshuffled. This creates new recodings that have the same sized groups as SR-6. The new recodings were, from random-00 to random-03:

MPKE AY IDGQ TRC WNLF SVH
EIFT WL QVPG NKM SCHA RYD
LCQS GK WPTI VRD YEFN MAH
IWQV TY KDLM ESH APCF GRN

To apply these to the data, each amino acid was replaced with the first amino acid in the group. When applying random-00, for example, each instance of R and C would be replaced by a T.

The 20 state matrices are the same across all analyses since they are not recoded. Since all the 20 state matrices are the same, variation between 20-state results (as in the left side of each pane of Figure 1B) give insight into the technical variance of the inference process.

Each new matrix was analyzed with PhyloBayes-mpi. Analyses were run for 1000 generations, and a 200 generation burnin applied. The resulting tree files and posterior predictive scores were parsed for display with the code in manuscript.rmd.

The statistics presented in Supplemental Figures 1 and 2 were parsed from the Feuda et al. manuscript into the file tidy_table_3.tsv and rendered for display with the code in manuscript.rmd.

Ackowledgements

We thank the Yale Center for Research Computing for use of the research computing infrastructure, specificaly the Farnam cluster.

Author contributions

Supplemental Information

Models of molecular evolution

The exchangeability matrix \(E\) describes the rate at which one amino acid changes to another. Exchangeability matrices have been used in the studies under consideration here include: Poisson (or F81), WAG, LG, GTR. While the exchangeability matrix describes the relative rate of different changes between amino acids, the actual rate can be further scaled. There are a couple approaches that have been used in the studies considered here:

  • F8129 corresponds to equal rates between all states. The F81 matrix is also sometimes referred to as the Poisson matrix. It has no free parameters to estimate since all off-diagonal elements are set to 1.

  • WAG30 is an empirically derived exchangeability matrix based on a dataset of 182 globular protein families. It has no free parameters to estimate since all off-diagonal elements are set according to values estimated from this particular sample dataset.

  • LG31, like WAG, is an empirically derived exchangeability matrix. It is based on a much larger set of genes, and variation in rates across sites was taken into consideration when it was calculated. It has no free parameters to estimate since all off-diagonal elements are set according to values estimated from this particular sample dataset.

  • GTR, the General Time Reversible exchangeability matrix, has free parameters for all off-diagonal elements that describe the exchangeability of different amino acids. It is constrained so that changes are reversible, i.e. the rates above the diagonal are the same as those below the diagonal. This leaves 190 parameters that must be estimated from the data long with the other model parameters and the phylogenetic tree topology. This estimation requires a considerable amount of data and computational power, but if successful has the advantage of being based on the dataset at hand rather than a different dataset (as for LG and WAG).

While the exchangeability matrix describes the relative rate of different changes between amino acids, the actual rate can be further scaled. There are a couple approaches that have been used in the studies considered here:

  • Site homogeneous rates. The rates of evolution are assumed to be the same at all sites in the amino acid alignment.

  • Gamma rate heterogeneity. Each site is assigned to a different rate class with its own rate value. This accommodates different rates of evolution across different sites. Gamma is used so commonly that sometimes it isn’t even specified, making it difficult at times to know if a study uses Gamma or not.

The vector of equilibrium frequencies \(\Pi\) describes the stationary frequency of amino acids. There are a few approaches that have been used across the studies considered here:

  • Empirical site-homogeneous. The frequency of each amino acid is observed from the matrix under consideration and applied to homogeneously to all sites in the matrix.

  • Estimated site-homogeneous. The frequency of each amino acid is inferred along with other model parameters, under the assumption that it is the same at all sites.

  • CAT site heterogeneous3. Each site is assigned to a class with its own equilibrium frequencies. The number of classes, assignment of sites to classes, and equilibrium frequencies within the data are all estimated in a Bayesian framework.

  • C10 to C60???. 10 to 60-profile mixture models as variants of the CAT model under the maximum-likelihood framework.

  • Recoding method: Amino acids are recoded into six groups based on function to account for both compositional heterogeneity and substitution saturation. Several recoding strategies have been proposed, including Dayhoff 6-state recoding, S&R 6-state recoding, KGB 6-state recoding based on exchangeability matrix of models.

Details of published analyses

Dunn et al. 2008

Dunn et al.2 added Expressed Sequence Tag (EST) data for 29 animals. It was the first phylogenomic analysis that included ctenophores, and therefore that could test the relationships of both Ctenophora and Porifera to the rest of animals. It was also the first phylogenetic analysis to recover Ctenophora as the sister group to all other animals.

The data matrix was constructed using a semi-automated approach. Genes were translated into proteins, promiscuous domains were masked, all gene sequences from all species were compared to each other with blastp, genes were clustered based on this similarity with TribeMCL32, and these clusters were filtered to remove those with poor taxon sampling and high rates of lineage-specific duplications. Gene trees were then constructed, and in clades of sequences all from the same species all but one sequence were removed (these groups are often due to assembly errors). The remaining gene trees with more than one sequence for any taxon were then manually inspected. If strongly supported deep nodes indicative of paralogy were found, the entire gene was discarded. If the duplications for a a small number of taxa were unresolved, all genes from those taxa were excluded. Genes were then realigned and sites were filtered with Gblocks33, resulting in a 77 taxon matrix. Some taxa in this matrix were quite unstable, which obscured other strongly-supported relationships. Unstable taxa were identified with leaf stability indices34, as implemented in phyutility35, and removed from the matrix. This resulted in the 64-taxon matrix that is the focus of most of their analyses. Phylogenetic analyses were conducted under the F81+CAT model in phylobayes3, and under the WAG model in MrBayes36 and RAxML37.

Regarding the recovery of Ctenophora-sister, the authors concluded:

The placement of ctenophores (comb jellies) as the sister group to all other sampled metazoans is strongly supported in all our analyses. This result, which has not been postulated before, should be viewed as provisional until more data are considered from placozoans and additional sponges.

Note that there was, in fact, an exception to strong support. An analysis of the 40 ribosomal proteins in the matrix recovered Ctenophora-sister with only 69% support. This study did not include Trichoplax.

Philippe et al. 2009

Philippe et al. 2009 [Philippe:2009hh] assembled an EST dataset for 55 species with 128 genes to explore phylogenetic relationship of early diverging animals. The phylogenetic analysis using Poisson-CAT model strongly supported Porifera-sister, and ctenophores was sister to cnidarians to form the “coelenterate” clade. The authors concluded: > The resulting phylogeny yields two significant conclusions reviving old views that have been challenged in the molecular era: (1) that the sponges (Porifera) are monophyletic and not paraphyletic as repeatedly proposed, thus undermining the idea that ancestral metazoans had a sponge-like body plan; (2) that the most likely position for the ctenophores is together with the cnidarians in a ‘‘coelenterate’’ clade.

Hejnol et al. 2009

Hejnol et al. 2009 added EST sequences from seven taxa, and a total of 94 taxa were included in the final data matrix. The orthology inference was largely simialr to Dunn et al. 2008. Two datsets were constructed, one with 844 genes and the other with 330 genes. Maximum likelihood analyses from both datasets strongly supported Ctenophora-sister.

Pick et al. 2010

Pick et al.38 sought to test whether Ctenophora-sister was an artefact of insufficient taxon sampling. They added new and additional published sequence data to the 64-taxon matrix of Dunn et al.2. The new taxa included 12 sponges, 1 ctenophore, 5 cnidarians, and Trichoplax. They further modified the matrix by removing 2,150 sites that were poorly sampled or aligned. They considered two different sets of outgroups: Choanoflagellata (resulting in Choanimalia) and the same sampling as Dunn et al. (resulting in Opisthokonta).

All their analyses were conducted under the F81+CAT+Gamma model in phylobayes3, in both a Bayesian framework and with bootstrapping. All analyses have the same ingroup sampling and site removal so it isn’t possible to independently assess the impact of these factors. Analyses with Choanimalia sampling recovered Porifera-sister with 72% posterior probability (PP) and 91% bootstrap support (BS). With broader Opisthokonta sampling, support for Porifera-sister is 84% PP. This is an interesting case where increased outgroup sampling leads to increased support for Porifera-sister.

The authors argue that previous results supporting Ctenophora-sister “are artifacts stemming from insufficient taxon sampling and long-branch attraction (LBA)” and that “this hypothesis should be rejected”. Although the posterior probabilities supporting Porifera-sister are not strong, they conclude:

Results of our analyses indicate that sponges are the sister group to the remaining Metazoa, and Placozoa are sister group to the Bilateria

They also investigated saturation, and conclude that Dunn et al.2 is more saturated than Philippe et al. 2009 [Philippe:2009hh]. Note that the Pick et al.38 dataset is not reanalyzed here because partition data are not available, and due to site filtering the partition file from Dunn et al.2 cannot be applied to this matrix.

Ryan et al. 2013

Ryan et al. 2013 sequenced the first ctenophore genome of Mnemiopsis leidyi. With the genome resources of M. leidyi, the authors constructed two phylogenomic datasets: a “Genome set” based on 13 animal genomes and a “EST Set” that also included 59 animals. They analyzed both matrices by site-homogeneous GTR+Gamma model and Poisson-CAT model with three sets of outgroup sampling to evaluate the effect of outgroup selection to the ingroup topology. The results strongly supported Ctenophora-sister in all datasets they analysed using site-homogeneous model. The Poisson + CAT model of the genome dataset strongly supported of a clade of Ctenophora and Porifera as the sister group to all other Metazoa and Bayesian analysis on the EST dataset did not converge after 205 days.

Moroz et al. 2014

Moroz et al. 2014 sequenced the second ctenophore genome Pleurobrachia bachei to explore the phylogenetic relationship of Metazoa. All phylogenetic analyses strongly supported Ctenophora-sister with different taxon and gene sampling using WAG site-homogeneous model.

Whelan et al. 2015

Wehlan et al. 2015 constructed a new phylogenomic dataset by eight new transcripomic data and investigated a range of possible sources of systematic error under multiple analyses (e.g. long-branch attraction, compositional bias, fast evolving genes, etc.). Putative orthologs were determined of each species using HaMStR using the model organism core ortholog set (~ 1000 orthologs) and subsequetly removal of genes with too much missing data and potential paralogs. The authors further filtered the full dataset to 24 sub-datasets by filtering genes with high long-branch scores; genes with high RSFV values; genes that are potential paralogs; fast evolving genes and progressively removal of outgroups. All the maximum likelihood analyses with site-homogeneous model and PartitionFinder strongly suggested Ctenophora-sister. CAT-GTR models only used in least saturated dataset 6 and 16 also strongly supported Ctenophora. Regarding the recovery of Ctenophora-sister, the authors concluded: > Importantly, biases resulting from elevated compositional heterogeneity or elevated substitution rates are ruled out. Placement of ctenophores as sister to all other animals, and sponge monophyly, are strongly supported under multiple analyses, herein.

Chang et al. 2015

Chang et al. 2015 was originally used to explore phylogenetic position of Myxozoa in Cnidaria but also sampled broadly across the breadth of animal diversity. The authors constructed a dataset with 200 protein markers based on Philippe et al. 2011 with 51,940 amino acids and 77 taxa. Both site-heterogeneous Poisson-CAT and site-homogeneous GTR models strongly supported Ctenophora-sister.

Pisani et al. 2015

Pisani et al. 2015 reanalyzed representative datasets that supported Ctenophora-sister, including Ryan2013, Moroz2014 and Whelan2015 datasets. It was the first study showing that progressively removal of more distantly related outgroups could largely affect phylogenomic inference of animal-root position. The authors suggested that the inclusion of outgroups very distant from the ingroup can cause systematic errors due to long-branch attraction. They found strong support of porifera-sister when only include Choanozoa as outgroup in these datasets using site-heterogeous CAT model. Regarding the recovery of Porifera-sister, the authors concluded: >Our results reinforce a traditional scenario for the evolution of complexity in animals, and indicate that inferences about the evolution of Metazoa based on the Ctenophora-sister hypothesis are not supported by the currently available data.

Feuda et al. 2017

Feuda et al. didn’t generate any new data, instead they used the data-recoding methods to reanalyze two key datasets that support Ctenophora-sister (Whelan2015_D20, Chang2015 datasets). It was the first study phylogenomic analysis using recoding strategy to explore animal-root position. The authors compared model adequacy using posterior predictive analyses from a set of site-homogeneous (WAG, LG, GTR, PartitionFinder)and site-heterogeneous (CAT-GTR) models with non-recoding and recoding datasets. The results showed that data-recoding can significant reduce compositional heterogeneity in both datasets with CAT-GTR models and strongly supported Porifera-sister hypothesis. Regarding the recovery of Porifera-sister, the authors concluded:

Because adequate modeling of the evolutionary process that generated the data is fundamental to recovering an accurate phylogeny, our results strongly support sponges as the sister group of all other animals and provide further evidence that Ctenophora-sister represents a tree reconstruction artifact.

Whelan and Halalnych 2017a

Whelan et al. is the only study to evaluate performance of site-heterogeneous models and site-homogeneous model with data partitioning under the simulation framework. The simulation results suggested that Poisson+CAT model consistently performed worse than other models in simulation datasets. More importantly, the authors also showed that both Poisson + CAT and GTR + CAT models could overestimated substitutional heterogeneity in many cases. They also reanalyzed datasets from Philippe 2009 and Nosenko 2013 using both CAT models and data partitioning with site -homogeneious model. The results indicated that Poisson + CAT model tends to recover less accurate trees and both GTR + CAT and data partitioning strongly supported Ctenophora-sister in reanalyses. The authors also concluded:

Practices such as removing constant sites and parsimony uninformative characters, or using CAT-F81 when CAT-GTR is deemed too computationally expensive, cannot be logically justified. Given clear problems with CAT-F81, phylogenies previously inferred with this model should be reassessed.

Whelan et al. 2017b

Whelan et al. added 27 new ctenophore transcriptomic data to explore animal-root position as well as relationships within Ctenophores. It significantly increased ctenophore taxon sampling than other studies. Putative orthologs were determined largely similar to Whelan2015, with the difference of a core Ctenophora core datasets were constructed here with more than 2000 genes. The subsequent filtering strategy was also similar to the previous study. All analyses using site-homogeneous and site-heterogeneous models strongly supported Ctenophora-sister hypothesis, even with CAT-GTR model in Choanoazoa dataset. Regarding the recovery of Ctenophora-sister, the authors concluded: >Using datasets with reasonably high ctenophore and other non-bilaterian taxon sampling, our results strongly reject the hypothesis that sponges are the sister lineage to all other extant metazoans.

Simion et al. 2017

Simion et al.2 added transcriptomic data for 21 new animals. The data matrix was constructed using a semi-automated approach to comprehensively detect and eliminate potential systematic errors. The resulting dataset comprises 1,719 genes and 97 species, including 61 non-bilaterian metazoan species. It was by far the largest phylogenomic dataset in terms of taxon and gene sampling related to animal-root question.

The supermatrix was first analyzed using the Poisson-CAT model. Different that other phylobayes analyses, Simion et al. used a gene jackknife strategy based on 100 analyses to overcome the computational limitation because of the large data size. Each jackknife is baed on a random selection of ~ 25% of the genes. The Phylobayes with site-heterogeneous model strongly supported the Porifera-sister, whereas site-homogeneous strongly supported Ctenophora-sister in the supermatrix dataset. Importantly, the authors compared the behavior of long-branch effect between site-heterogenious and site-homogeneous models by progressively removal taxa and concluded higher sensitivity of site-homogeneous models to LBA than CAT models. Regarding the recovery of Ctenophora-sister, the authors concluded:

Our dataset outperforms previous metazoan gene superalignments in terms of data quality and quantity. Analyses with a best-fitting site-heterogeneous evolutionary model provide strong statistical support for placing sponges as the sister-group to all other metazoans, with ctenophores emerging as the second-earliest branching animal lineage.

Data-recoding methods

Feuda et al.28 tackled a difficult phylogenetic problem – placing the root of the animal phylogeny39. In the past decade, some analyses have placed Ctenophora (comb jellies) and others Porifera (sponges) as the sister group to all other animals (Fig XXRandom_recodingA). Feuda et al.28 present new phylogenetic analyses that they claim provide strong support for Porifera-sister. Their analyses consider datasets from studies by Chang et al.40 and Whelan et al.41, both of which found support for Ctenophora-sister. Feuda et al. were concerned that these Ctenophora-sister results were artefacts of lineage-specific differences in amino acid frequencies. In an attempt to reduce these differences, they recoded the full set of twenty amino acids into six groups of amino acids. These groups have more frequent evolutionary changes within them than between them, based on empirical observations in large protein datasets27. The intent is to discard many lineage-specific changes, which are expected to fall within these groups. Rather than model compositional heterogeneity, as their title suggests, this approach discards heterogeneous information so that much simpler models with fewer states can be applied. They report that posterior predictive (PP) analyses21 indicate 6-state recoded analyses have better model adequacy than 20-state amino acid analyses, and “Porifera-sister was favored under all recoding strategies”. Here we focus on two aspects of Feuda et al. First, we point out that many of their recoded analyses are actually unresolved (i.e., without strong support for either Porifera-sister or Ctenophora-sister), and that the analyses with the best posterior predictive scores do not provide strong support for Porifera-sister. Second, we present new analyses that show the impact of recoding is largely due to discarding information, not accommodating variation in amino acid composition. These findings indicate that it is premature to accept Porifera-sister and reject Ctenophora-sister. They also show that recoding can be a problematic method for addressing compositional variation.

Feuda et al. examine support for Ctenophora-sister and Porifera-sister under all combinations of two models of molecular evolution, four datasets, and four coding schemes. This provides 32 analyses that they report in their Table 3 and that we present graphically here as Supplemental Fig XXRecoidng_summary. There is striking variation in support for Ctenophora-sister and Porifera-sister across these analyses (Fig XXRecoidng_summary). Feuda et al. accept the results of some analyses and reject others based on posterior predictive (PP) analyses of model adequacy, which assess how well a model explains variation in the data21. They considered five different posterior predictive statistics that capture different types of variation in the data. From this they conclude that their “results strongly support sponges as the sister group of all other animals”.

This conclusion does not follow from their own presented results. Only a single analysis with posterior predictive scores provides what could be considered strong support > 95 posterior probability) for Porifera-sister. Of the 32 analyses, posterior predictive scores were calculated for 16 (those for the full Whelan and Chang matrices). Based on posterior predictive scores, Feuda et al. reject eight of these that were conducted under the GTR+G model (which all have strong support for Ctenophora-sister). This leaves eight CAT-GTR+G analyses (Fig XXRecoding_pp). Two of these eight are analyses of the original 20-state amino acid data, both of which provide strong support for Ctenophora-sister. Of the six recoded analyses, five are unresolved. Only a single analysis for which posterior predictive scores are available provides strong support for Porifera-sister, the CAT-GTR+G analysis of the SR-627 recoded Whelan41 matrix. Furthermore, this analysis does not have the best score according to any of the five posterior predictive statistics they considered (Fig XXRecoding_pp). The only statistic that stands out for this one analysis is that it has the highest maxdiff (Fig XXRecoding_pp), indicating that it did not converge as well as other analyses.

Though their study does not provide strong support for Porifera-sister, the sensitivity of their results to recoding provides an opportunity to better understand and evaluate the impact of recoding more generally. This is important given the growing interest in recoding28. Feuda et al. hoped recoding would reduce potential artefacts due to differences across species in amino acid frequencies. They interpreted the fact that their analyses are sensitive to recoding as evidence that such an artefact exists and that they successfully addressed it by recoding. An alternative hypothesis is that recoding impacts phylogenetic analyses because it discards so much information. These two hypotheses can be tested by applying new recoding schemes that also reduce twenty states down to six, but are based on random grouping rather than empirical frequencies of amino acid exchange. Empirical and random recodings both discard the same amount of information, but only empirical recoding reduce the impact of amino-acid frequency as intended. Different results between empirical and random recoding would be consistent with the hypothesis that the empirical approach works as intended to accommodate compositional heterogeneity. Similar results would suggest that the impact of recoding is due to discarding information. Here we focus on their single analysis with a posterior predictive score that supports Porifera-sister, the CAT-GTR+G analysis of the SR-6 recoded Whelan data. we created four new random recoding schemes by shuffling the amino acids in the SR-6 scheme (see Supplemental Methods and analysis code at https://github.com/caseywdunn/feuda_2017). When we applied each of these randomized codes to the Whelan matrix and analyzed them under the CAT-GTR+G model with phylobayes-mpi26, we observed similar results as for the empirical SR-6 recoding. Like SR-6 recoding, random recoding increases support for Porifera-sister and improves the apparent adequacy of models to explain heterogeneity of states across taxa (PP taxon hetero mean and max, Fig XXRandom_recodingB).

These analyses suggest that the major impact of recoding on phylogenetic analyses is data reduction, not accommodation of compositional heterogeneity across species. This indicates that recoding may not be an effective tool for accommodating among-species differences in amino acid frequencies. Compositional heterogeneity would be better addressed with models of molecular evolution that explicitly describe such differences42, if progress can be made on the considerable computational challenges of such complex models.

Fig XXRandom_recoding. (A) The two alternative hypotheses for deep animal relationships considered here. Relationships that are not part of these hypotheses are shown as unresolved polytomies. (B) Each of the six plots presents one statistic, which include Posterior probability of Porifera-sister and the five Posterior Predictive (PP) statistics considered by Feuda et al. Within each plot, there are six lines for six different analyses. These six analyses are the published SR-6 analyses presented by Feuda et al. (SR6-published), analyses obtained by applying the same methods to the same data to to confirm that I can reproduce their published results (SR6-reproduced), and four analyses based on randomized recoding matrices obtained by shuffling the SR-6 coding scheme (random-00 – random-03). Each analysis includes results for 20 states (the raw amino acid data, shown by the left point) and for 6 states (the 6-state recoded data, shown by the right point). For each statistic, the results obtained with the random recoding are similar to those of the SR6 recoding. This indicates that the impact of recoding is dominated by discarding data when collapsing from 20 states to 6 states, not accommodating compositional heterogeneity across lineages.

Fig XXRecoidng_summary. A graphical representation of the posterior probabilities for the 32 analyses presented by Feuda et al. in their Table 3. Cells are color coded by whether posterior probability is > 95 for Porifera-sister, > 95 for Ctenophora-sister, or neither (unresolved). Posterior predictive (PP) statistics were estimated for the 16 analyses in the top two rows of this figure (the Chang and full Whelan matrices), but not the bottom two (the Whelan matrices with reduced outgroup sampling).

Fig XXRecoding_pp The subset of eight CAT-GTR+G analyses with posterior predictive (PP) scores that is the focus of Feuda et al.’s primary conclusions. These are a subset of the 32 analyses presented in their Table 3 and graphically here in the upper right pane. The eight analyses are for two datasets (Chang and Whelan) and four coding schemes. The coding schemes are the original 20 state amino acid data, and three different six state recodings that group amino acids based on different criteria: KGB6, D6, and SR6. Cells are color coded as in Supplemental Fig XXRecoidng_summary. Only one of these analyses, the SR6 coding of the Whelan matrix, has > 95 support for Porifera-sister. The 20-state and 6-state points on the plots in Fig XXRecoidng_summary of the main text correspond to the 20 and SR6 Whelan cells shown here. The presented statistics for these cells are posterior probability of Porifera-sister, Maxdiff (with lower scores indicating better convergence of runs), and five posterior predictive statistics (where lower absolute value indicates better model adequacy). The only one of these eight analyses that provides strong support for Porifera-sister is not the most adequate analysis by any of the posterior predictive scores, and showed the poorest convergence according to Maxdiff.

Matrix mapping

Taxa and partition correspondence across manuscripts was assessed by comparing all sequences for each taxon in each partition across all matrices with diamond blast. Based on inspection of sequence similarity, we excluded all comparisons with less than 99% identity and greater than 10-25 e-value.

Taxa comparison across matrices

The primary intent of comparing taxa across matrices was to validate our taxon name reconciliation across studies.

We first considered pairwise similarity between the same species from different matrices in different studies.

Partition comparison across matrices

The count for a partition pair can be much larger than the number of genes in the matrix, which suggests that the count is the number of hsps rather than the number of sequences with hits.

There are 17 matrices. A gene that is perfectly sampled would form a cluster with this size. To our surprise, very few clusters, though, are this size. This suggests that intersection of genes between matrices is extreme low. This finding suggested genes used for exploring the position of the root of the animal phylogeny

References

1.Wallberg, A., Thollesson, M., Farris, J. & Jondelius, U. The phylogenetic position of the comb jellies (Ctenophora) and the importance of taxonomic sampling. Cladistics 20, 558–578 (2004).

2.Dunn, C.W., Hejnol, A., Matus, D.Q., Pang, K., Browne, W.E., Smith, S.A., Seaver, E., Rouse, G.W., Obst, M., Edgecombe, G.D., Sørensen, M.V., Haddock, S.H.D., Schmidt-Rhaesa, A., Okusu, A., Kristensen, R.M., Wheeler, W.C., Martindale, M.Q. & Giribet, G. Broad phylogenomic sampling improves resolution of the animal tree of life. Nature 452, 745–749 (2008).

3.Lartillot, N. A Bayesian Mixture Model for Across-Site Heterogeneities in the Amino-Acid Replacement Process. Molecular Biology and Evolution 21, 1095–1109 (2004).

4.Feuda, R., Dohrmann, M., Pett, W., Philippe, H., Rota-Stabelli, O., Lartillot, N., Wörheide, G. & Pisani, D. Improved modeling of compositional heterogeneity supports sponges as sister to all other animals. Current Biology 27, 3864–3870 (2017).

5.Waterhouse, R.M., Seppey, M., Simão, F.A., Manni, M., Ioannidis, P., Klioutchnikov, G., Kriventseva, E.V. & Zdobnov, E.M. BUSCO applications from quality assessments to gene prediction and phylogenomics. Molecular biology and evolution 35, 543–548 (2017).

6.Pisani, D., Pett, W., Dohrmann, M., Feuda, R., Rota-Stabelli, O., Philippe, H., Lartillot, N. & Wörheide, G. Genomic data do not support comb jellies as the sister group to all other animals. Proceedings of the National Academy of Sciences 112, 15402–15407 (2015).

7.Philippe, H., Brinkmann, H., Lavrov, D.V., Littlewood, D.T.J., Manuel, M., Wörheide, G. & Baurain, D. Resolving difficult phylogenetic questions: Why more sequences are not enough. PLoS biology 9, e1000602 (2011).

8.Chang, E.S., Neuhof, M., Rubinstein, N.D., Diamant, A., Philippe, H., Huchon, D. & Cartwright, P. Genomic insights into the evolutionary origin of myxozoa within cnidaria. Proceedings of the National Academy of Sciences 112, 14912–14917 (2015).

9.Whelan, N.V., Kocot, K.M., Moroz, L.L. & Halanych, K.M. Error, signal, and the placement of ctenophora sister to all other animals. Proceedings of the National Academy of Sciences 112, 5773–5778 (2015).

10.Hernandez, A.M. & Ryan, J.F. Six-state amino acid recoding is not an effective strategy to offset the effects of compositional heterogeneity and saturation in phylogenetic analyses. BioRxiv 729103 (2019).

11.Hejnol, A., Obst, M., Stamatakis, A., Ott, M., Rouse, G.W., Edgecombe, G.D., Martinez, P., Baguñà, J., Bailly, X., Jondelius, U. & others Assessing the root of bilaterian animals with scalable phylogenomic methods. Proceedings of the Royal Society B: Biological Sciences 276, 4261–4270 (2009).

12.Borowiec, M.L., Lee, E.K., Chiu, J.C. & Plachetzki, D.C. Extracting phylogenetic signal and accounting for bias in whole-genome data sets supports the ctenophora as sister to remaining metazoa. BMC genomics 16, 987 (2015).

13.Whelan, N.V., Kocot, K.M., Moroz, T.P., Mukherjee, K., Williams, P., Paulay, G., Moroz, L.L. & Halanych, K.M. Ctenophore relationships and their placement as the sister group to all other animals. Nature ecology & evolution 1, 1737 (2017).

14.Simion, P., Philippe, H., Baurain, D., Jager, M., Richter, D.J., Di Franco, A., Roure, B., Satoh, N., Queinnec, E., Ereskovsky, A. & others A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Current Biology 27, 958–967 (2017).

15.Lanier, H.C. & Knowles, L.L. Is recombination a problem for species-tree analyses? Systematic Biology 61, 691–701 (2012).

16.Salichos, L. & Rokas, A. Inferring ancient divergences requires genes with strong phylogenetic signals. Nature 497, 327 (2013).

17.Shen, X.-X., Opulente, D.A., Kominek, J., Zhou, X., Steenwyk, J.L., Buh, K.V., Haase, M.A., Wisecaver, J.H., Wang, M., Doering, D.T. & others Tempo and mode of genome evolution in the budding yeast subphylum. Cell 175, 1533–1545 (2018).

18.Fernández, R., Kallal, R.J., Dimitrov, D., Ballesteros, J.A., Arnedo, M.A., Giribet, G. & Hormiga, G. Phylogenomics, diversification dynamics, and comparative transcriptomics across the spider tree of life. Current Biology 28, 1489–1497 (2018).

19.Whelan, N.V. & Halanych, K.M. Who let the cat out of the bag? Accurately dealing with substitutional heterogeneity in phylogenomic analyses. Systematic biology 66, 232–255 (2016).

20.Shen, X.-X., Hittinger, C.T. & Rokas, A. Contentious relationships in phylogenomic studies can be driven by a handful of genes. Nature Ecology & Evolution 1, 0126 (2017).

21.Bollback, J.P. Bayesian model adequacy and choice in phylogenetics. Molecular Biology and Evolution 19, 1171–1180 (2002).

22.Buchfink, B., Xie, C. & Huson, D.H. Fast and sensitive protein alignment using diamond. Nature Methods 12, 59 EP (2014).

23.Hagberg, A.A., Schult, D.A. & Swart, P.J. Exploring network structure, dynamics, and function using networkx. Proceedings of the 7th python in science conference 11–15 (2008).

24.Kalyaanamoorthy, S., Minh, B.Q., Wong, T.K., Haeseler, A. von & Jermiin, L.S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nature methods 14, 587 (2017).

25.Nguyen, L.-T., Schmidt, H.A., Haeseler, A. von & Minh, B.Q. IQ-tree: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Molecular biology and evolution 32, 268–274 (2014).

26.Lartillot, N., Rodrigue, N., Stubbs, D. & Richer, J. PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Systematic biology 62, 611–615 (2013).

27.Susko, E. & Roger, A.J. On reduced amino acid alphabets for phylogenetic inference. Molecular Biology and Evolution 24, 2139–2150 (2007).

28.Feuda, R., Dohrmann, M., Pett, W., Philippe, H., Rota-Stabelli, O., Lartillot, N., Wörheide, G. & Pisani, D. Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals. Current Biology 27, 1–12 (2017).

29.Felsenstein, J. Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution 17, 368–376 (1981).

30.Whelan, S. & Goldman, N. A General Empirical Model of Protein Evolution Derived from Multiple Protein Families Using a Maximum-Likelihood Approach. Molecular Biology and Evolution 18, 691–699 (2001).

31.Le, S.Q. & Gascuel, O. An improved general amino acid replacement matrix. Molecular Biology and Evolution 25, 1307–1320 (2008).

32.Enright, A., Van Dongen, S. & Ouzounis, C. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research 30, 1575–1584 (2002).

33.Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Molecular Biology and Evolution 17, 540–552 (2000).

34.Thorley, J. & Wilkinson, M. Testing the phylogenetic stability of early tetrapods. Journal of Theoretical Biology 200, 343–344 (1999).

35.Smith, S.A. & Dunn, C.W. Phyutility: a phyloinformatics tool for trees, alignments and molecular data. Bioinformatics 24, 715–716 (2008).

36.Ronquist, F. & Huelsenbeck, J.P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).

37.Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690 (2006).

38.Pick, K.S., Philippe, H., Schreiber, F., Erpenbeck, D., Jackson, D.J., Wrede, P., Wiens, M., Alie, A., Morgenstern, B., Manuel, M. & Worheide, G. Improved phylogenomic taxon sampling noticeably affects nonbilaterian relationships. Molecular Biology and Evolution 27, 1983–1987 (2010).

39.King, N. & Rokas, A. Embracing Uncertainty in Reconstructing Early Animal Evolution. Current biology : CB 27, R1081–R1088 (2017).

40.Chang, E.S., Neuhof, M., Rubinstein, N.D., Diamant, A., Philippe, H., Huchon, D. & Cartwright, P. Genomic insights into the evolutionary origin of Myxozoa within Cnidaria. Proceedings of the National Academy of Sciences 1–6 (2015).doi:10.1073/pnas.1511468112

41.Whelan, N.V., Kocot, K.M., Moroz, L.L. & Halanych, K.M. Error, signal, and the placement of Ctenophora sister to all other animals. Proceedings of the National Academy of Sciences 112, 201503453–6 (2015).

42.Blanquart, S. & Lartillot, N. A Site- and Time-Heterogeneous Model of Amino Acid Replacement. Molecular Biology and Evolution 25, 842–858 (2008).

43.Foster, P.G. Modeling compositional heterogeneity. Systematic biology 53, 485–495 (2004).